Consider a regression problem with examples described by 2 continuous attributes, x and y. each example is sampled according to the uniform distribution on (−1,1)2 and labeled with f(x, y)=1−x2−y2. a learner’s hypothesis class is h(x, y)=ax+by+c. calculate its bias and variance as a function of x and y if the learner sees an arbitrarily large training sample.